Artificial intelligence in healthcare is maturing along a spectrum, ranging from targeted machine learning models to more ambitious projects aiming for broader cognitive capabilities. Narrow AI, which includes familiar techniques such as machine learning and natural language processing, is well established and underpins many current healthcare applications. Meanwhile, the industry continues to explore more generalized AI that could handle a wider variety of tasks traditionally reserved for human experts. While these ambitions fuel innovation, most healthcare organizations remain focused on concrete, incremental improvements to existing processes. Deploying AI in real-world healthcare settings faces persistent challenges. Integrating new models with legacy systems, safeguarding patient data, and navigating an evolving regulatory framework require cross-functional teams and continued investment. Larger organizations have made better progress moving AI projects from proof-of-concept to production, but integration and security remain the top barriers. Data privacy and regulatory compliance are not simply hurdles, but priorities that shape how AI initiatives are designed and governed. Here, we break down how Cotiviti, Edifecs, and our customers are applying AI to support two critical initiatives: payment integrity and value-based payment.
Artificial intelligence in healthcare is maturing along a spectrum, ranging from targeted machine learning models to more ambitious projects aiming for broader cognitive capabilities. Narrow AI, which includes familiar techniques such as machine learning and natural language processing, is well established and underpins many current healthcare applications. Meanwhile, the industry continues to explore more generalized AI that could handle a wider variety of tasks traditionally reserved for human experts. While these ambitions fuel innovation, most healthcare organizations remain focused on concrete, incremental improvements to existing processes.
Deploying AI in real-world healthcare settings faces persistent challenges. Integrating new models with legacy systems, safeguarding patient data, and navigating an evolving regulatory framework require cross-functional teams and continued investment. Larger organizations have made better progress moving AI projects from proof-of-concept to production, but integration and security remain the top barriers. Data privacy and regulatory compliance are not simply hurdles, but priorities that shape how AI initiatives are designed and governed.
Here, we break down how Cotiviti, Edifecs, and our customers are applying AI to support two critical initiatives: payment integrity and value-based payment.
